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Online Serving V11

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lzh222333
2021-05-14 06:44:16 +00:00
parent d71a666904
commit ebd01e0de5
21 changed files with 326 additions and 230 deletions
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qlib/workflow/online/strategy.py
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@@ -2,7 +2,7 @@
# Licensed under the MIT License.
"""
OnlineStrategy is a set of strategy for online serving.
OnlineStrategy module is an element of online serving.
"""
from copy import deepcopy
@@ -19,7 +19,7 @@ from qlib.workflow.task.utils import TimeAdjuster
class OnlineStrategy:
"""
OnlineStrategy is working with `Online Manager <#Online Manager>`_, responsing how the tasks are generated, the models are updated and signals are perpared.
OnlineStrategy is working with `Online Manager <#Online Manager>`_, responding to how the tasks are generated, the models are updated and signals are prepared.
"""
def __init__(self, name_id: str):
@@ -28,7 +28,7 @@ class OnlineStrategy:
This module **MUST** use `Trainer <../reference/api.html#Trainer>`_ to finishing model training.
Args:
name_id (str): a unique name or id
name_id (str): a unique name or id.
trainer (Trainer, optional): a instance of Trainer. Defaults to None.
"""
self.name_id = name_id
@@ -40,29 +40,29 @@ class OnlineStrategy:
After the end of a routine, check whether we need to prepare and train some new tasks based on cur_time (None for latest)..
Return the new tasks waiting for training.
You can find last online models by OnlineTool.online_models.
You can find the last online models by OnlineTool.online_models.
"""
raise NotImplementedError(f"Please implement the `prepare_tasks` method.")
def prepare_online_models(self, models, cur_time=None) -> List[object]:
def prepare_online_models(self, trained_models, cur_time=None) -> List[object]:
"""
Select some models from trained models and set them to online models.
This is a typically implementation to online all trained models, you can override it to implement complex method.
You can find last online models by OnlineTool.online_models if you still need them.
This is a typical implementation to online all trained models, you can override it to implement the complex method.
You can find the last online models by OnlineTool.online_models if you still need them.
NOTE: Reset all online models to trained model. If there is no trained models, then do nothing.
NOTE: Reset all online models to trained models. If there are no trained models, then do nothing.
Args:
models (list): a list of models.
cur_time (pd.Dataframe): current time from OnlineManger. None for latest.
cur_time (pd.Dataframe): current time from OnlineManger. None for the latest.
Returns:
List[object]: a list of online models.
"""
if not models:
if not trained_models:
return self.tool.online_models()
self.tool.reset_online_tag(models)
return models
self.tool.reset_online_tag(trained_models)
return trained_models
def first_tasks(self) -> List[dict]:
"""
@@ -87,7 +87,7 @@ class OnlineStrategy:
class RollingStrategy(OnlineStrategy):
"""
This example strategy always use latest rolling model as online model.
This example strategy always uses the latest rolling model sas online models.
"""
def __init__(
@@ -99,11 +99,11 @@ class RollingStrategy(OnlineStrategy):
"""
Init RollingStrategy.
Assumption: the str of name_id, the experiment name and the trainer's experiment name are same one.
Assumption: the str of name_id, the experiment name, and the trainer's experiment name are the same.
Args:
name_id (str): a unique name or id. Will be also the name of Experiment.
task_template (Union[dict,List[dict]]): a list of task_template or a single template, which will be used to generate many tasks using rolling_gen.
name_id (str): a unique name or id. Will be also the name of the Experiment.
task_template (Union[dict, List[dict]]): a list of task_template or a single template, which will be used to generate many tasks using rolling_gen.
rolling_gen (RollingGen): an instance of RollingGen
"""
super().__init__(name_id=name_id)
@@ -117,9 +117,10 @@ class RollingStrategy(OnlineStrategy):
def get_collector(self, process_list=[RollingGroup()], rec_key_func=None, rec_filter_func=None, artifacts_key=None):
"""
Get the instance of `Collector <../advanced/task_management.html#Task Collecting>`_ to collect results. The returned collector must can distinguish results in different models.
Assumption: the models can be distinguished based on model name and rolling test segments.
If you do not want this assumption, please implement your own method or use another rec_key_func.
Get the instance of `Collector <../advanced/task_management.html#Task Collecting>`_ to collect results. The returned collector must distinguish results in different models.
Assumption: the models can be distinguished based on the model name and rolling test segments.
If you do not want this assumption, please implement your method or use another rec_key_func.
Args:
rec_key_func (Callable): a function to get the key of a recorder. If None, use recorder id.
@@ -160,9 +161,9 @@ class RollingStrategy(OnlineStrategy):
def prepare_tasks(self, cur_time) -> List[dict]:
"""
Prepare new tasks based on cur_time (None for latest).
Prepare new tasks based on cur_time (None for the latest).
You can find last online models by OnlineToolR.online_models.
You can find the last online models by OnlineToolR.online_models.
Returns:
List[dict]: a list of new tasks.
@@ -198,7 +199,7 @@ class RollingStrategy(OnlineStrategy):
rec_list (List[Recorder]): a list of Recorder
Returns:
List[Recorder], pd.Timestamp: the latest recorders and its test end time
List[Recorder], pd.Timestamp: the latest recorders and their test end time
"""
if len(rec_list) == 0:
return rec_list, None